Mobile Computer Vision

Ego-Motion Compensated Face Detection on a Mobile Device
In this paper we propose face tracking on a mobile device by integrating an inertial measurement unit into a boosting based face detection framework.
Since boosting based methods are highly rotational variant, we use gyroscope data to compensate for the camera orientation by virtual compensation of the camera ego-motion.
The proposed fusion of inertial sensors and face detection has been tested on Apple's iPhone 4.
The tests reveal that the proposed fusion provides significant better results with only minor computational overhead compared to the reference face detection algorithm.
(pdf)
The App TNT Face Detection is now available in Apple's AppStore.

Viola & Jones

proposed

SlimCuts: GraphCuts for High Resolution Images Using Graph Reduction
This paper proposes an algorithm for image segmentation using GraphCuts which can be used to efficiently solve labeling problems on high resolution images or resource-limited systems. The basic idea of the proposed algorithm is to simplify the original graph while maintaining the maximum flow properties. The resulting Slim Graph can be solved with standard maximum flow/minimum cut-algorithms. We prove that the maximum flow/minimum cut of the Slim Graph corresponds to the maximum flow/minimum cut of the original graph. Experiments on image segmentation show that using our graph simplification leads to significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner even on resource-limited systems.